Unsupervised Anomaly Detection for Multivariate Time Series Using Diffusion Model

Unsupervised anomaly detection for multivariate time series (MTS) is a challenging task due to the difficulties of precisely learning the complex data patterns of MTS. The recent progress in sample generation achieved by diffusion models (DMs) motivates us to leverage the powerful learning ability of DMs to make a breakthrough in unsupervised anomaly detection for MTS. In this paper, we make the first attempt to design a novel diffusion-based anomaly detection model (named TimeADDM) for MTS data using the effective learning mechanism of DMs. To enhance the learning effect on MTS data, we propose to apply diffusion steps to the representations that accumulate the global time correlations through recurrent embedding. To enable the model for accurate anomaly detection, we design a reconstruction strategy that uses various levels of diffusion to compute the anomaly scores from different angles. By comparing TimeADDM with the state-of-the-art benchmarks, the results demonstrate that TimeADDM outperforms all baselines in terms of detection accuracy in four real-world MTS datasets and makes an improvement on the F1 score by up to 22%. The codes of the experiments with datasets and our algorithms are available at https://github.com/Hurongyao/TIMEADDM.